Einleitung

Running is one of the most popular sports for the masses with 13.9 million road race finishers in the U.S. in 20111. The fact that almost everyone can run might be an explanation for the increasing popularity of long distance running. Especially during these long distance runs, it is very important to run efficiently, i.e. to not waste energy [6], as the energy saved in each step adds up during the thousands of steps taken [4]. Among others, biomechanical factors were found to influence running economy [8]. The use of wearable sensors has become more and more popular for athletes and trainers [3]. They allow monitoring movement unobtrusively during a running workout [7]. To date, most available applications for runners focus on monitoring the final performance, i.e. speed or distance, rather than providing information on performance determining factors. Commercially available systems include the Nike+ iPod kit2, the Garmin Forerunner3, and the adidas micoach4. The primary purpose of these systems is to log workouts and motivate the runner. A smartphone-based feedback-providing system to improve running technique might be especially useful to ambitioned fitness runners who train on their own and do not have access to a trainer. In this work, we developed a smartphone application to assess a runner’s economy using a simple spring mass model [1]. Movement was measured using an inertial measurement unit (IMU) attached to the runner’s hip. Our proposed system was able to provide the runner with a real-time feedback. We investigated two different feedback modalities and evaluated our system within a user study including 7 runners. This work is structured as follows: First, we present our algorithms and the measurement device. Afterwards, we give a brief overview on the developed application and describe the feedback provision. Finally, the user study with results and the conclusion are presented.